Earth and Environmental Sciences

Abstract Climate models project that the Atlantic Meridional Overturning Circulation (AMOC) will weaken in the 21st century, but the magnitude is highly uncertain. Some of this uncertainty is structural, as most climate models neglect increasing meltwater from the Greenland ice sheet and do not explicitly capture mesoscale ocean eddies. Here, we quantify the impact of Greenland meltwater on the AMOC until 2100 under SSP5‐8.5 forcing for the first time in a strongly eddying (1/10° horizontal resolution) ocean model. The meltwater‐induced additional AMOC weakening is small (0.6 0.2 Sv) compared to the weakening due to warming alone, and similar at high and low resolution. The same meltwater would cause a stronger AMOC weakening under present‐day climate conditions. We link both resolution‐independence and state‐dependence to large‐scale controls of the AMOC. Our results demonstrate that the background ocean state is more important than resolution in determining how Greenland meltwater affects the AMOC.

Summary Sediment transport in high mountain environments is affected by the interplay of hydro‐meteorological dynamics, erosion processes, and climate‐driven landscape change, such as glacier retreat and mountain greening. Mountain catchments in High Mountain Asia (HMA) are prone to debris flow hazard due to the steep terrain, intense monsoon precipitation, and rapid cryosphere degradation. Catchments susceptible to debris flow events have previously been classified as having either transport‐limited or supply‐limited sediment regimes. However, the contribution of glacier meltwater, precipitation extremes, changing vegetation cover, and sediment availability, on debris flow activity remains poorly quantified across environments in the region. Here, we modified the Sediment Cascade (SedCas) model to simulate water balance components and subsequent sediment transport and debris flow events of a conceptual debris‐flow catchment in two monsoon‐dominated regions of the Central Himalaya: Langtang (humid, monsoon‐dominated) and Mustang (semi‐arid, rain‐shadow), Nepal. We consider a total of 55 scenarios reflecting transitions from glaciated to vegetated terrain and a set of sediment supply scenarios and intra‐annual erosion patterns. Our results show that glacier melt more strongly affects runoff and debris flow magnitude in drier, continental climate, while increases in vegetation and a reduction in glacier cover substantially decrease potential sediment supply and shift debris flow events to later in the monsoon season. The number of annual debris flow events remains relatively stable across scenarios, but their magnitude and seasonality are altered by both sediment recharge (i.e., sediment supply recovery) timing and land cover evolution. The modeling experiments show that as the landscape changes (through glacier retreat and vegetation succession), transport‐limited and supply‐limited conditions become more similar, suggesting that future debris flows may become smaller. These findings highlight the importance of hydro‐geomorphic feedbacks under rapid environmental change to understand sediment‐related hazards in high‐relief Asian mountain regions.

Background As climate warming persists, heat-related health risks continue to escalate. This study aims to forecast the future community-level burden of heat-attributable hospital admissions and emergency department (ED) visits in Victoria, Australia, under multiple climate, population, and adaptation scenarios. Methods We estimated the associations between high temperature exposure and daily hospital admissions and ED visits during the hot seasons of 2014–2019 in Victoria. These estimated associations were then applied to projected future climate and population scenarios to quantify the future heat-related health burden. Results Under all future climate change scenarios, the heat-attributable burden of hospital admissions and ED visits was projected to rise substantially over time. Compared with 2020–2029, excess heat-related hospital admissions and ED visits during 2090–2099 were expected to increase by 74% and 67%, respectively, under a low-emission scenario (Shared Socioeconomic Pathway [SSP]1–2.6), and by 1177% and 423%, respectively, under the high-emission scenario (SSP5–8.5). The corresponding heat-attributable rates are projected to increase by 11% and 7% under SSP1–2.6, and by 453% and 126% under SSP5–8.5 for hospital admissions and ED visits, respectively. Although adaptation may partially alleviate the health burden of heat, pronounced increasing trends remained evident. Further analysis revealed consistently higher heat-related burdens of hospital admissions and ED visits in rural areas compared to urban areas, with the urban-rural disparity expected to widen over time. Conclusion Rising temperatures are projected to increase the burden of heat-related hospital admissions and ED visits, particularly in rural areas. This underscores the urgent need for climate change mitigation efforts and targeted public health interventions to reduce future health risks.

Earth's landscapes-from mountains to river deltas-are shaped by sediment erosion, transport, and deposition. However, most landscape evolution models represent only two processes: diffusive soil creep and bedrock river incision. How much of Earth's surface can be represented by these two processes alone? To address this question, we mapped the global abundance and distribution of eight major geomorphic process domains, finding that ~75% of Earth's land area consists of soil-mantled hillslopes and flatlands dominated by diffusive soil creep, while 0.5% is a bedrock river corridor. While other process domains (glaciers, lakes, alluvial river corridors, aeolian deserts, and bedrock hillslopes) make up only ~24% of Earth's land area, they transport most of the sediment from continents to oceans. Process domains align with different topographic, climatic, and tectonic conditions, indicating a pathway to improve coupled global landscape, climate, and tectonic evolution models. Overall, our results reveal the distribution of the eight major geomorphic process domains globally and highlight the spatial dominance of diffusive topography.

In this study, a hybrid deep learning and ensemble-based approach is introduced to predict typhoon intensity accurately by utilizing historical meteorological track data. The model is compounded by Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to comprehensively learn spatial and temporal features of typhoon patterns. It first uses CNN to extract spatial features of typhoon track at-tributes (such as wind speed, pressure, latitude, and longitude) and time series attributes and the LSTM network captures time series features and sequential dynamics of typhoon motions. To mitigate the class imbalance problem due to the much lower number of severe typhoon instances, the Synthetic Minority Oversampling Technique (SMOTE) is utilized for balancing the dataset and for achieving better generalization of the model. In addition, a Random Forest (RF) classifier is adopted as ensemble component to further improve the robustness of classification and predictive performance. The model is tested on the Kaggle Typhoon Track Dataset, in which the typhoon intensity is divided into three ranks: Low, Moderate, and Severe. The expire-mental results show that the proposed hybrid CNN–LSTM–RF–SMOTE framework obtains an overall classification accuracy of 97.1% with F1-scores of 0.910, 0.955 and 0.992 for the Low, Moderate and Severe class, respectively. The results of experiments on real data demonstrate that the proposed HST-LSTM+ model can effectively capture the complex spatiotemporal dependencies of typhoon track data and subsequently improve the reliability of a prediction on the typhoon intensity. This prediction ability may contribute to the disaster preparedness, risk management, and early warning system for extreme weather.

Abstract. Snow cover over the Tibetan Plateau (TP) is not only a key land forcing for the regional and global climate but also an important water resource for surround regions. However, state-of-the-art climate models still exhibit substantial biases in simulating winter snow cover over the TP, which constitutes one of the major sources of uncertainty in climate prediction. Using satellite-based snow cover datasets, this study reveals that the Community Land Model version 5 (CLM5) systematically overestimates the winter snow cover fraction (SCF) over the TP. This bias mainly arises because the original SCF parameterization scheme neglects the spatially varying probability distribution of snowfall accumulation and underestimates snow depletion over barren land during the melting period. By accounting for the effects of non-growing-season low vegetation (i.e., withered grass stems) and topographic relief, we parameterize the snow accumulation probability factor (kaccum) instead of prescribing it as a constant. In addition, a revised factor is introduced to modify the snow depletion curve shape parameter (Nmelt), thereby optimizing the SCF parameterization scheme. Preliminary validation indicates that the optimized scheme substantially reduces positive winter SCF biases over the entire TP by 63 %, and improves surface albedo simulations, thereby alleviating cold surface temperature biases by approximately 1–2 °C in snow-affected regions.

Abstract Accurate projection of future precipitation remains challenging due to uncertainties in reference data sets, bias correction and global climate models (GCMs). Here, we evaluated these uncertainties across 13 major cities of the U.S. Gulf Coast, a hurricane‐prone region, under 192 historical and future scenarios. Four reference gridded precipitation data sets, eight CMIP6 GCMs and one HighResMIP model (CMCC‐CM2‐VHR4) were first evaluated against in situ measurements. All GCMs were then bias corrected using four reference data sets and two statistical techniques, empirical quantile mapping (EQM) and a hybrid EQM with linear correction (EQM‐LIN). The bias corrected outputs were then evaluated against in situ measurements. All gridded data sets, HighResMIP and GCMs tended to overestimate light events but underestimate extremes. PRISM and CPC showed the strongest and weakest agreement, respectively, while AORC outperformed others across the Southwest Florida Peninsula, where land‐sea interactions and spatial heterogeneity challenge coarse‐resolution models. Bias correction substantially improved model performance up to the 90th percentile, reducing MAE and RMSE by more than 70% in some cases. However, the performance degraded beyond this percentile; very high percentiles (≥95th) remained underestimated. Future projections under SSP2‐4.5 and SSP5‐8.5 indicated that bias correction reduces inter‐model spread of extreme precipitation indices (Rx1day, SDII and R95p) by approximately 60%–80%, while HighResMIP projections generally remained within the CMIP6 ensemble range. These findings highlighted that credible projections of future precipitation depend more on the representativeness and quality of reference data sets and bias correction technique than the GCMs. The results provide guidance for improving future precipitation projections, updating intensity‐duration‐frequency curves and advancing resilience planning in hurricane‐prone regions.

Groundwater extraction can deplete streamflow in headwater catchments, but the complexity of subsurface hydrological processes make impacts difficult to detect. Using hydrograph-inferred hillslope groundwater storage and streamflow relationships, we propose a novel approach to estimate streamflow depletion from groundwater pumping that is well-suited to areas with limited groundwater monitoring infrastructure. We apply this method in two well-studied watersheds in California’s North Coast to quantify potential hydrologic impacts of cannabis agriculture, which is concentrated in the region and has been identified as a potential threat to salmon-bearing streams. We use a scenario-based approach to explore the relative effects of cannabis cultivation area, irrigation water source (groundwater pumping vs. surface diversion), irrigation efficiency, stream discharge at the onset of the growing season, and lithology on streamflow depletion risk. Our models show that Elder Creek , a perennial stream, could be de-watered by the late dry season with high levels (1% land cover) of cannabis irrigation from groundwater when dry season discharge is low at the start of the season (1 mm/day). In Dry Creek , a non-perennial stream, dry season flow cessation could be advanced by five weeks from similar levels of cannabis water demands. Streamflow impacts are more pronounced in drier years, and the impacts from well-water extraction exhibit a muted effect relative to surface water diversion of the same volume. Storage-discharge functions like those in our case study can estimate how groundwater extraction affects headwater streams wherever streamflow data exist

Abstract. Top-down atmospheric CO2 inversions are essential for estimating surface carbon fluxes, yet significant inter-system discrepancies highlight an incomplete understanding of how observational information is transferred to flux estimates. This study introduces a diagnostic strategy to explicitly investigate this information transfer, primarily in an Ensemble Kalman Filter (EnKF) system, with a comparative analysis of 4D-Var. Using Monte Carlo simulations, we analyze the spatial and temporal correlation patterns between CO2 concentrations and fluxes, which play a crucial role in the inversion process by tracing information flow via the influence matrix. Our results reveal that these correlation scales are fundamentally set by the prescribed autocorrelation structure of the prior fluxes, rather than by atmospheric transport processes alone. We identify a resonance-like effect wherein correlated fluxes amplify concentration-flux correlations, while uncorrelated fluxes suppress them. The absence of this suppression for prescribed fluxes (e.g., anthropogenic emissions) can cause systematic signal misattribution. We further demonstrate that 4D-Var relies also heavily on flux autocorrelations due to its cost function's localized gradient. In both methods, the prior's critical role is mediated through the transitivity of strong autocorrelations. Simplified observing system simulation experiments corroborate these diagnostic findings: under the current sparse surface network in East Asia, a relatively longer correlation length (e.g., 600 km) is better than a short length (e.g., 100 km). This process-oriented perspective offers practically useful mechanistic insights for reconciling inversion results, optimizing observing networks, and strengthening carbon budget assessments.

During geomagnetic storms, ionospheric disturbances can undergo substantial spatiotemporal restructuring and affect high-precision GNSS applications. This study investigates the multiscale ionospheric response over Brazil during the intense geomagnetic storm of 12 November 2025 and examines the associated changes in precise point positioning (PPP) convergence. Multi-source observations, including GNSS TEC/dSTEC, ROTI, JPL Global Ionospheric Maps, ionosonde parameters, and three-dimensional ionospheric tomography, were jointly analyzed. The results show that the storm produced pronounced and nonuniform global TEC anomalies, with the Brazilian sector embedded in a disturbed background. Over Brazil, clear traveling ionospheric disturbance (TID) propagation and ROTI enhancement were observed during the main response phase. The TID developed after approximately 02:05 UT and reached its maximum intensity during 02:25–03:00 UT. Ionosonde observations indicated decreased foF2 and increased h′F2, suggesting electron density depletion and an apparent uplift of the F-region reflection height. The GNSS dSTEC-constrained tomographic reconstruction suggested that the relative perturbation structures were more evident at 150–400 km, especially near 250–350 km. PPP analysis further revealed longer convergence times on the storm day, particularly in the vertical component. These results indicate that the Brazilian ionosphere experienced a multiscale response from global anomalies to regional propagation and vertical restructuring, which was associated with delayed PPP convergence performance.

Abstract Reliable large‐sample hydrological predictions require systematic benchmarking and careful model selection. However, this process is challenging due to structural uncertainty among models, high computational demand, and the climatic and physiographic diversity of a region. This study benchmarked 47 conceptual rainfall–runoff models across 159 watersheds in Peninsular India using the Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) and applied a consistent performance threshold to identify plausible simulations. The benchmarking framework enabled systematic evaluation of model adequacy, complexity, and uncertainty, followed by selection of a compact set of models. Results showed that 141 watersheds met the performance threshold, although model performance varied considerably, with semi‐arid regions posing the greatest challenge and tropical regions exhibiting high equifinality. A compact set of five models (HBV, GR4J, MODHYDROLOG, SMA, and Hillslope) was found sufficient to reproduce reliable streamflow in over 85% of watersheds, while an additional five models extended robust flood prediction to the same coverage. Storage analysis revealed that soil moisture was the dominant process representation, followed by routing and interception storages, and the most reliable models required no more than three storages. Parameter complexity also influenced performance: models with intermediate complexity (10–15 parameters) generally performed best, although some lower‐parameter models, such as GR4J, also achieved strong performance. Model skill was further associated with watershed characteristics, including soil porosity, forest cover, and leaf area index. Overall, the results demonstrate that a small, well‐chosen set of models with appropriate storage structures and moderate complexity can reliably represent hydrological processes across Peninsular India.

Abstract Microplastics (MP) are increasingly recognized as important contaminants of agricultural soils. Yet a lack of global estimates of MP inputs to agricultural land hampers systematic quantification of both current contamination levels and accumulation over time at scales. Here, we introduce MPGRIDSv1, the first global geospatial dataset of MP inputs into agricultural soils via the application of MP-contaminated organic matter (OM) used as fertilizer. We collected, through a literature survey, the OM amendment rates, A, and the MP concentration, C, in OM for 17 and 26 countries, respectively. Upon assumptions and simplifications for OM composition, and type and appearance of MPs, we estimated high and low values of A and C for 186 countries using statistical inference methods built on 14 independent national-level covariates including waste management, agricultural practices, and environmental and socio-economic factors. High and low estimates of A and C, and the annual mass of MP inputs estimated for countries worldwide were then spatialized on selected fruit, vegetable and pulse crops in the CROPGRIDSv1 dataset. Globally, 33 to 2,316 (910 median) thousand tonnes of MP were estimated to reach agricultural soils annually, with input rates ranging from less than 0.01 to more than 100 kg-MP ha-1 year-1 worldwide. While these numbers underestimate reality and acknowledging several limitations including paucity of data and uncertainty in existing ones, we expect MPGRIDSv1 to facilitate analyses of agricultural soil degradation globally with geospatial detail.

Abstract The Last Glacial Maximum (LGM) and Last Interglacial (LIG) provide insights into the response of the North Atlantic–European climate variability to strongly contrasting boundary conditions. Using six CMIP6/PMIP4 models, we compare temperature–precipitation patterns, atmospheric circulation, and variability, with emphasis on the North Atlantic Oscillation (NAO). Across climate states, the leading modes of wintertime sea-level pressure variability remain recognisable, indicating a robust dynamical structure. However, their amplitude, explained variance, and spatial positioning exhibit state dependence. During the LGM, extensive ice sheets were associated with a stronger and more spatially confined NAO dipole, southward-displaced pressure centres, and reorganised circulation patterns, reinforcing colder and drier European conditions with enhanced seasonality. The LIG exhibits a weaker NAO expression with westward-shifted pressure centres, a more zonal circulation, northward-shifted warm sea surface temperatures, and enhanced seasonality. NAO-related interannual atmosphere–ocean coupling persists across climate states, with limitations in capturing low-frequency variability within the available simulation lengths. Summer variability is substantially weaker and more heterogeneous across all periods, with the NAO losing dominance often to the East Atlantic pattern during the LGM. Inter-model differences increase considerably under LGM and LIG conditions compared to the pre-industrial period (PI), underscoring the importance of multi-model approaches and an improved representation of key processes when investigating climate variability in extreme climate states.

Cocoa production in Ghana and Côte d’Ivoire is threatened by climate variability and extremes, particularly droughts and excessive rainfall. However, quantitative evidence on the impacts of climate change on cocoa yields remains limited, constraining the development of effective climate-smart adaptation strategies. This study assessed future climate impacts on cocoa production using regridded (~5 km resolution) ensemble projections from 12 Global Climate Models under a low (SSP1-2.6) and a high (SSP5-8.5) SSP scenario. Precipitation and temperature data were bias-corrected using five approaches: Delta Change, CDFt, SDM, EQM, and LOCI. Among these, the Delta Change method best preserved intra-annual climate variability, while temperature corrections outperformed precipitation corrections. A Random Forest model, trained on bias-corrected climate data, simulated and projected cocoa yields with an accuracy exceeding 85%, although performance varied across regions. Future changes were assessed for the near future (2026–2055) and far future (2056–2085) relative to a historical baseline (1985–2014). Ensemble projections indicate a drying trend across cocoa-growing areas, with precipitation declining by 5–10% under SSP5-8.5 and increasing modestly (around 5%) under SSP1-2.6. At the same time, temperatures are projected to rise across all regions, exceeding 3.5°C under SSP5-8.5 by the late century, particularly in central and northern zones. Projected yield responses vary spatially. Southern and coastal cocoa-growing areas are expected to experience yield declines of about 5%, with losses reaching up to 20% under severe drought conditions in highly vulnerable regions such as Dix-Huit Montagnes in Côte d’Ivoire under SSP5-8.5. In contrast, some northern and central regions may maintain or slightly increase yields under SSP1-2.6. Vulnerability is shaped by climatic, biophysical, and socio-economic factors, with regions such as Sud-Comoé (Côte d’Ivoire) and Brong Ahafo (Ghana) identified as at risk. These findings highlight the need for targeted adaptation strategies to enhance the resilience of West Africa’s cocoa sector.

Abstract This study quantified the modulating effects of ice-phase microphysical processes on surface precipitation intensity in typhoon spiral rainbands using convection-permitting numerical simulations (3-km grid spacing) of Super Typhoon Lekima (2019). The Weather Research and Forecasting model with Morrison double-moment microphysics scheme was employed to diagnose ice process rates and their contributions to surface precipitation. The simulated 48-hour accumulated precipitation was validated on a common 0.1° grid against GPM IMERG (V07B) retrievals, and the simulated peak accumulations along the Zhejiang coast were further compared with regional automatic weather station gauges, supporting the model’s representation of the rainband structure. Results revealed that ice-phase processes dominated precipitation formation, contributing 68–92% of surface rainfall through melting of ice particles below the freezing level. Graupel melting emerged as the primary mechanism, accounting for 52% of surface precipitation in convective cores, followed by snow melting (31%) and cloud ice-origin particles (17%). Systematic differences between inner and outer rainbands were identified, with inner rainbands exhibiting 40% higher ice water content and enhanced graupel production due to stronger updrafts. The storm-centered environmental shear remained within a moderate regime of roughly 11–14 m s −1 throughout the analysis period, and against this large-scale background a grid-point sensitivity analysis showed that vertical wind shear and mid-level humidity strongly modulated ice-phase precipitation efficiency, with moderate shear reducing efficiency from 0.78 to 0.52. Pronounced diurnal variations and marked transformations during landfall highlighted complex interactions between thermodynamic, dynamic, and microphysical processes. These findings provide insights for improving typhoon precipitation forecasts and underscore the importance of accurately representing ice-phase processes in operational weather prediction models for typhoon-affected regions.

The escalating frequency and severity of marine heatwaves are driving factors behind mass coral bleaching, necessitating both rapid emission reductions and the development of scalable intervention tools. Shading coral to reduce irradiance stress is a promising approach, yet most studies focus on static structures, and few include recovery dynamics. Here, we tested the efficacy of a laboratory-scale seawater fogging system as a shading intervention during a simulated thermal/light stress and recovery experiment. An orthogonal design with two temperature levels (control (26.5 °C) vs heat-stressed ((MMM) at the collection site, 29.1 + 3.7 °C) and two light treatments (fogged, 33.3% shading ± 6.46 SD for 6 h daily vs non-fogged) was used to test the response of Acropora hyacinthus and Pocillopora damicornis over 13 days of thermal stress (3 °C-weeks) followed by a 24-day recovery period. Variable shading from seawater fog reduced mortality risk in heat-stressed A. hyacinthus by 55%. Only two mortalities occurred in P. damicornis , both in the heat-stressed treatment without fog. Fog lowered per cent whiteness and improved Fv/Fm in both species, starting at 0.84 °C-weeks, with the effects peaking near 3 °C-weeks. Fogging during the recovery period did not inhibit coral recovery but provided some additional benefits, including increased Fv/Fm and lower catalase activity. Seawater fog can not only be used to delay/reduce bleaching while thermal stress is high but can also enhance recovery and post-stress repair processes of corals. In order to advance seawater fogging as a practical reef intervention tool, engineering advances should be accompanied by in-situ trials that capture complex coral-environment interactions and assess ecosystem-wide responses.

Accelerating climate change and chronic anthropogenic pressures demand connectivity-based design of marine protected area networks (MPANs). Yet integrating dynamic species responses and cumulative human pressures into spatial planning remains a key challenge, particularly in marginal seas at the land–ocean interface. As a biodiversity hotspot under intensive development, the Yellow and Bohai Seas (YBS) exemplify this complexity, yet how global change will reshape connectivity here remains unclear. Using ensemble species distribution models, we projected suitable habitats for 70 representative marine taxa under present-day conditions and two future scenarios (SSP1-2.6 and SSP5-8.5, representing low- and high-emission Shared Socioeconomic Pathways), generating multi-species natural resistance surfaces. These were combined with an entropy-weighted anthropogenic resistance layer—comprising fishing, shipping, mariculture, and pollution—to produce integrated resistance surfaces (IRS) for each scenario. Circuit theory was then applied to quantify ecological corridors, current density, pinch points, and barriers among 61 MPAs. Our results showed that: (1) climate forcing drove pronounced habitat redistribution, with cold-adapted taxa contracting within the Bohai Sea while warm-adapted species shifted poleward, especially under SSP5-8.5; (2) the IRS intensified overall (mean rising from 1.00 to 1.22), especially in the central-northern Bohai, while the southern Yellow Sea—particularly the central trough influenced by the Yellow Sea Cold Water Mass (YSCWM)—retained relatively low resistance, indicating a potential climatological corridor; (3) although total corridor length increased modestly (6,937 to 7,634 km), functional connectivity declined sharply, with Critical Corridors decreasing from 66 to 36 and effective resistance rising by 27%; (4) Grade-1 pinch points expanded by 35% and barrier clusters nearly tripled in area, concentrating where climate-degraded habitats overlap with intense human activities. Together, these drivers create a high-resistance Bohai core and more permeable Yellow Sea margins—a north–south divergence with direct consequences for species persistence and climate-driven range shifts. Embedding global change-adjusted corridor, pinch-point, and barrier metrics into spatial planning is therefore essential for maintaining MPA network resilience in the YBS and other marginal seas.

Extreme storm tide levels, arising from nonlinear cross−scale interactions among surge, astronomical tide, and fluvial flood, threaten estuarine stability and cause major economic losses. The Bay-Inlet-Channel (BIC) system, pivotal to the Greater Bay Area, was severely impacted by Typhoon Hato, which produced record−breaking winds and severe inundation. To quantify the morphological-hydrodynamic resilience of the BIC system against the highest storm tide level (HSTL), the Delft3D model was employed to reproduce characteristics of Hato. Simulation results indicate that HSTL exhibited a sharp gradient along the Bay, with a relative increase of 63.84%, and a more moderate one in the Channel (37.00%), associated with the Channel’s higher resilience ( R G = 0.87). Under a hypothetical “Triple Coincidence” scenario involving a stronger flood discharge, the robustness of the BIC system decreased, with a more pronounced decline for the Channel (Δ R G = −0.23) than for the Bay. Contribution analysis identified surge as the dominant driver of HSTL during Hato (52–75%), followed by astronomical tide (25–51%), nonlinear interactions (−7–6%), and flood (<2%). Surge dominance diminished under “Triple Coincidence” as nonlinear interactions intensified. Momentum and energy analyses showed that lateral HSTL differences were primarily governed by direct wind stress, while longitudinal variations were modulated by morphological heterogeneity. Stronger floods amplified HSTL unevenly, mainly through enhanced nonlinear convection. These discoveries advanced the understanding of the morphological-hydrodynamic resilience and its mechanism regulating HSTL in estuarine systems, providing insights for storm tide risk management.

Abstract. The Characterising CiRrus and icE cloud acrosS the specTrum-Microwave (CCREST-M) aircraft campaign (February–March 2024) was based around the Chilbolton Observatory, UK, using the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 aircraft together with ground-based multi-frequency radars to provide a testbed for ice-cloud scattering and radiative transfer models across the microwave and sub-millimetre spectrum. Ice particle size distributions (PSDs) are retrieved from the ground-based zenith-pointing radars at the time of the radiometric overpasses, and the aircraft in-situ PSDs are used as an independent validation dataset. We present a novel hybrid retrieval framework for mid-latitude ice PSD parameters (slope λ, intercept No, and shape μ of the gamma size distribution) that combines a machine-learning (ML) ensemble with physics-based multi-frequency radar retrievals using 3, 35, and 94 GHz reflectivities. An ensemble of ML models is trained on observations from the Parameterising Ice Clouds using Airborne ObServationS and triple-frequency dOppler radar (PICASSO) campaign, also centred on Chilbolton Observatory. These models predict PSD moments from temperature, pressure, 3 GHz-retrieved ice water content (IWC), and the mean mass-weighted dimension. The ML predictions are converted into first guess gamma-PSD parameters at each height. A subsequent deterministic optimisation then adjusts No and λ, using a randomly oriented rosette-aggregate scattering model, to enforce simultaneous agreement with the observed 35 and 94 GHz reflectivities. Application of the above method to three CCREST-M cases show that the ML ensemble reproduces PSD moments well for two cases but fails when extrapolating beyond its trained temperature range in the third case. Retrieved IWCs from the 3 GHz radar compare favourably with in-situ measurements of IWC, and exponential (μ=0) and gamma PSD assumptions show comparable performance overall. Retrieved mean PSDs show generally good agreement with in-situ PSDs as a function of temperature for two of the cases, with IWCs within about 50 % of the in-situ measured IWCs over much of the −50 to −10 °C temperature range. The systematic biases seen in one case are attributed to temporal cloud evolution between radar and in-situ sampling. Independent validation using 200 GHz radar reflectivity profiles shows good agreement between the forward-modelled refllectivities and measurements above about 4.5 km. Below 4.5 km the agreement is more sparse owing to the likely presence of dendritic particles, which depart from the rosette-aggregate scattering assumption.

Abstract Hydrological droughts in Alpine regions are increasingly shaped by human regulation, with hydropower operations playing a central role in modifying their statistical characteristics. This study examines how the representation of hydropower systems in hydrological models influences the identification and statistical characterization of drought. Using the HYPERstreamHS hydrological model, we simulate streamflow in the Adige River basin under three configurations of increasing complexity, ranging from natural flow conditions, to simplified rule‐based operations, to detailed representations of reservoir management based on historical operation patterns. Drought events are analyzed from both univariate and bivariate perspectives, with the latter relying on copula‐based frequency analysis to capture the joint statistical behavior of drought duration and severity. Our findings show that conventional performance metrics, such as the Nash–Sutcliffe Efficiency and its logarithmic variant, can mask substantial differences in drought statistics across model configurations. In particular, simplified or naturalized representations systematically underestimate the frequency of hydrological droughts, failing thus to reproduce the observed dependence structure between hydrological drought attributes. Only the configuration that explicitly incorporates reservoir infrastructure and realistic operational rules is able to replicate both the marginal distributions and the joint behavior of observed droughts. These results demonstrate that hydropower regulation not only alters streamflow regimes but also reshapes the statistical properties of droughts. Capturing this influence is essential for drought risk assessment in regulated basins. More broadly, the study highlights the limitations of simplified modeling approaches and underscores the need to integrate realistic storage reservoir operations into hydrological models to support decision‐making under drought conditions.

Abstract Although planted forests (PFs) contribute greatly to soil and water conservation in the Yangtze River Basin and are central to afforestation efforts in China, their resilience to compound drought and heatwave (CDHW) events remains poorly understood. The unprecedented CDHW event in 2022 provided a unique opportunity to assess the responses of PFs and natural forests (NFs) to such extreme climatic disturbances. Using kernel‐normalized difference vegetation index (kNDVI), gross primary productivity (GPP), and solar‐induced chlorophyll fluorescence (GOSIF), we found that PFs experienced larger declines than NFs during the event year, but recovered more rapidly in the following year under alleviated climatic conditions, revealing a clear resistance–recovery trade‐off. For both kNDVI and GPP, NFs showed weaker declines and PFs showed faster recovery in more than 70% of grids. NFs, with their taller canopies, greater biomass, and higher species diversity, exhibited stronger resistance, whereas PFs, dominated by simpler and younger stands, demonstrated higher recovery potential. Recovery differences between the two forest types became most evident under extreme and exceptional CDHW conditions. XGBoost coupled with SHAP analysis further showed that structural differences between forest types, particularly canopy height differences, were the strongest predictors of recovery divergence, while the effects of edaphic, compositional, and climatic variables varied among indicators. These findings provide new empirical evidence that NFs and PFs play complementary roles in ecosystem stability and recovery, offering critical implications for water resource management under the projected intensification of CDHWs.

Abstract Bayesian inference offers a flexible framework for parameter estimation and uncertainty quantification in eco‐hydrological models. However, simultaneously achieving robust posterior exploration and high computational efficiency for multimodal, high‐dimensional, and computationally intensive targets remains challenging for the widely used Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. In this study, we developed the Parallel Adaptive Transition Particle Evolution Metropolis Sequential Monte Carlo (PATPEMS) algorithm, which is an adaptive and parallel SMC sampler for posterior distributions of model parameters in offline calibration. PATPEMS employs an adaptive sequence of intermediate distributions to control weight degeneracy and automatically select stages, a flexible scheduling of MCMC proposal kernels used to rejuvenate particles, together with reflection boundary handling to maintain particle diversity, and a particle‐level parallelization scheme to exploit multicore architectures and reduce wall‐clock time for computationally intensive models. Performance is assessed on four case studies: two synthetic targets probing multimodality and high‐dimensional dependence, and a land surface model (LSM) with six parameters constrained by synthetic and real observations. Across all cases, PATPEMS provides close approximations to the target posteriors, judged against the analytic ground truth or reference solutions. For the LSM, parallelization yields substantial wall‐clock speedups over the original non‐parallel implementation. Compared with the original particle evolution Metropolis sequential Monte Carlo (PEM‐SMC) algorithm, these results indicate that PATPEMS provides a more adaptive and parallel framework for robust Bayesian calibration of multimodal, correlated, and computationally demanding land surface and environmental models.

Cryospheric landforms play a critical role in alpine hydrology and ecosystems. Using historical and contemporary data spanning nearly six decades (1967–2024), we assessed elevation change for glaciers, rock glaciers, and perennial snowfields and the thermal response of streams in the Teton Range, Wyoming, United States. Glaciers and snowfields thinned at −0.84 ± 0.07 meters per year (m year −1 ) and −0.59 ± 0.04 m year −1 between 2014 and 2022, a ~7-fold increase relative to 1967–2014, driven by warming summer temperatures. In contrast, rock glaciers are near equilibrium (−0.05 ± 0.05 m year −1 ) and saw no change in rate. Since 2015, snowfield-fed streams have warmed rapidly (+3.4°C), whereas glacier- and rock glacier–fed streams have warmed at lower magnitudes (+0.9° and +0.6°C, respectively). Our results demonstrate the greater resilience of rock glaciers to atmospheric warming, highlighting the critical role that these features will play as glaciers and perennial snowfields are lost.

Abstract Oceanic mesoscale eddies are pivotal in redistributing mass and heat. Despite a warming‐induced increase in global oceanic stratification, previous studies have reported a global increase in mesoscale eddy activity, particularly in eddy‐rich regions, pointing to a complex and regionally contingent response to climate change. Here we show that the eddy kinetic energy (EKE) over the two wings of the tropical Indo‐Pacific Ocean shows an opposite trend over the past three decades, with a pronounced decreasing in the western tropical Pacific Ocean (WTPO) but a significant increasing in the southeastern tropical Indian Ocean (SETIO). We find that the EKE decline in the WTPO is mainly due to weakened intra‐seasonal wind work, while the EKE increase in SETIO is mainly driven by weakened stratification in the north and enhanced shear in the south, both of which are associated with the intensified Indonesian Throughflow.